A 3-day bug hunt on a 3-person team costs up to β¬7,200 in lost engineering time. This workshop teaches you to prevent that β unit tests, data tests, and integration tests for PySpark and Databricks Lakeflow, including Spark Declarative Pipelines.
Checkpoint allows Spark to truncate dependencies on previously computed RDDs. In the case of streams processing their role is extended. In additional, they're not a single method to prevent against failures.
One of previous posts talked about checkpoint types in Spark Streaming. This one focuses more on one type of them - metadata checkpoint.
Bad things happen in distributed data processing and if we're prepared for them, it's better. To prevent against such issues Apache Spark is able to recompute failed partition but also to store the computation snapshot as a checkpoint. Both properties apply to GraphX module's fault-tolerance mechanism.
